INTRODUCTION

Europe is a continent largely situated in the northern hemisphere, and mostly in the east. It occupies the westernmost region of Eurasia and is bordered to the north by the Arctic Ocean, to the west by the Atlantic Ocean, to the south by the Mediterranean Sea and to the east by Asia. The eastern border consists of a broad and varied line of mountain ranges and waterways which would characterize a subcontinent as a statute. However, despite its great physical size and the weight of history and culture, Europe is usually granted the status of a full continent. It is the World’s sixth-largest continent.

Europe occupies nearly 10,180,000 square kilometers (3,930,000 square miles), or 2% of the Earth’s surface (6.8% of the land area). Politically, Europe is divided into about fifty sovereign nations, the largest and most populous of which are Russia, covering 39 percent of the continent and containing 15 percent of its population.As of 2018, Europe had a total population of about 741 million (about 11 percent of the world’s population). The European climate is largely influenced by warm Atlantic currents that balance winters and summers on a large part of the continent, except at latitudes where Asian and North American climate is extreme. Seasonal variations are more apparent outside the sea than close to the coast.

The Council of Europe was established in 1949 with the aim of unifying Europe in order to attain common objectives. Some states ’further European unification led to the creation of the European Union (EU), a separate political body between a union and a federation. The EU began in Western Europe but has spread to the east since the Soviet Union’s collapse in 1991. The currency of most European Union countries, the euro, is the most widely used by Europeans; and the Schengen region of the EU abolishes border checks and immigration controls for most of its member states.

Here, we have gathered data from European countries to compare their attributes on different parameters.

pollution = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/pollution_2016.csv')
env_satisfaction = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/env_satisfaction_2013.csv')
life_satisfaction = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/life_satisfaction_2013.csv')
politics = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/trust_in_politics_2013.csv')
income = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/median_income_2016.csv')
legal = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/trust_in_legal_2013.csv')
job_satisfaction = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/job_satisfaction_2013.csv')
budget = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/make_ends_meet_2016.csv')
crime = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/crime_2016.csv')
leisure_satisfaction = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/leisure_satisfaction_2013.csv')
underemployment = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/underemployment_2016.csv')
close_relations = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/close_relations_2015.csv')
low_savings = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/low_savings_2016.csv')
weather = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/weather.csv')
life_expectancy = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/life_expectancy_2016.csv')
unemployment = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/unemployment_2016.csv')
police = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/trust_in_police_2013.csv')
gdp = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/gdp_2016.csv')
health = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/perceived_health_2016.csv')
population = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/population_2011.csv')
work_hours = read.csv('C:\\College\\01_Subjects\\sem2\\visualisation\\assignment\\europe-datasets/work_hours_2016.csv')
MyMerge <- function(x, y){
  df <- merge(x, y, by= "country", all.x= TRUE, all.y= TRUE)
  return(df)
}
df <- Reduce(MyMerge, list(pollution,env_satisfaction,life_satisfaction,politics,income,legal,
            job_satisfaction,budget,crime,leisure_satisfaction,underemployment,
            close_relations,low_savings,weather,
            life_expectancy,unemployment,police,gdp,health,
            population,work_hours))

1. GDP vsJob vs Population

GDP_Job_Population <- ggplot(df, aes(x = gdp, y = prct_job_satis_high)) + 
  geom_point(aes(color = country, size = total_pop), alpha = 0.5) +
  #scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07")) +
  scale_size(range = c(0.5, 20), name="Total Population") +
  scale_fill_viridis(discrete=TRUE, guide=FALSE, option="A") +
    ylab("Job Satisfaction") +
    xlab("Gdp per Capita") +
  ggtitle("GDP vsJob vs Population")

ggplotly(GDP_Job_Population)

There is no clear relationship between GDP and job satisfaction, because there are several other unforeseeable variables involved in citizen worker satisfaction. As the highest GDP state Germany has a very poor work satisfaction. Denmark, Austria, Finland, Norway with a smaller population, and lower GDP in their states have very high job satisfaction.

2. Income vs Environment vs Population

Income_Env_Population <- ggplot(df, aes(x = median_income, y = prct_env_satis_high)) + 
  geom_point(aes(color = country, size = total_pop), alpha = 0.5) +
  #scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07")) +
  scale_size(range = c(0.5, 20), name="Total Population") +
  #scale_fill_viridis(discrete=TRUE, guide=FALSE, option="A") +
    xlab("Income") +
    ylab("Environment Satisfaction") +
    ggtitle("Income vs Environment vs Population")

ggplotly(Income_Env_Population)

This Bubble plot shows the relationship between Income, Satisfaction with the environment, Population of all European Countries. A country’s population is symbolized by the scale of the bubble. Germany also has a very high degree of environmental sustainability, income and population, while in all these three qualities Bulgaria is lagging behind.

Luxembourg is one of the wealthiest countries in Europe, with the highest wages in Europe, a very healthy and reasonable climate and a relatively small population level.

3. Life Expectancy vs Pollution

Life_Expectancy_crime_Population <- ggplot(df, aes(x = life_expect, y = prct_rpt_crime)) + 
  geom_point(aes(color = country, size = total_pop), alpha = 0.5) +
  scale_size(range = c(0.5, 20), name="Total Population") +
  #scale_fill_viridis(discrete=TRUE, guide=FALSE, option="A") +
    xlab("Life Expectancy") +
    ylab("crime") +
    ggtitle("Life Expectancy vs Pollution")

ggplotly(Life_Expectancy_crime_Population)

First of all, we can say that the life expectancy is about 75-85 across Europe, which is an outstanding life expectancy figure.
Compared with other European countries Bulgaria has a really high crime rate. Countries like Iceland, Croatia and Lithuania have a very low crime rate which makes it an attractive place for everyone

4. Hours worked vs Leisure Satisfaction

workhours_satisfaction_Population <- ggplot(df, aes(x = avg_hrs_worked, y = prct_leisure_satis_high)) + 
  geom_point(aes(color = country, size = total_pop), alpha = 0.5) +
  scale_size(range = c(0.5, 20), name="Total Population") +
  xlab("Hours Worked") +
    ylab("Leisure Satisfaction") +
    ggtitle("Hours worked vs Leisure Satisfaction")

ggplotly(workhours_satisfaction_Population)

Yet again, we can infer by pointing at the plot immediately that the working hours are within the ranges of 25-45 hours a week which is extremely fine.
Turkey is a poor choice because it is the country with the lowest levels of leisure and working hours.
Denmark is the best country for providing leisure for its people.

Geospatial Visualization

#reading shape file
europe_shp <- readOGR("C:\\Users\\sandy\\OneDrive\\Desktop\\ne_50m_admin_0_countries\\ne_50m_admin_0_countries.shp")
## OGR data source with driver: ESRI Shapefile 
## Source: "C:\Users\sandy\OneDrive\Desktop\ne_50m_admin_0_countries\ne_50m_admin_0_countries.shp", layer: "ne_50m_admin_0_countries"
## with 241 features
## It has 94 fields
## Integer64 fields read as strings:  POP_EST NE_ID
#subsetting data to make it for europe
europe_shp <- subset(europe_shp, is.element(europe_shp$NAME , df$country ))

#merging with data
spdf = merge(europe_shp ,df, by.y = "country", by.x = "NAME")

1. Unemployment Rate

tmap_mode("view")
legend_title = expression("Unemployment Rate")

if (require(RColorBrewer)) {
#pal <- brewer.pal(10, "Set2")[c(1, 8, 4, 5)]
pal <- c('#ff4d4d','#ccebff','#ffff1a','#1aff1a')
shape1 <- tm_shape(spdf)+
    tm_borders()+
    tm_polygons(col = 'unemp_rate', palette= pal ,title = legend_title)+
    tm_fill("unemp_rate", title = "Unemployment Rate") +
    tm_scale_bar(breaks = c(0, 100, 200), text.size = 1) +
    tm_view( set.zoom.limits = c(3,8), set.view = c(lon = 10, lat = 56, zoom = 3)) 

shape1
}
# barchart with added parameters
barplot(spdf$unemp_rate,
main = "Unemployment in European Countries",
ylab = "Unemployment Rate",cex.axis = .7,
names.arg = spdf$NAME, font.axis = 1, col.axis = "Red", las = 0,cex.names = .7,
col = "red",
las = 2)

Both, Greece and Spain have the highest level of unemployment, while all other European countries have very low rates of unemployment, in particular Germany, Iceland and Czech Republic.

2. Political Trust

legend_title = "political trust rating"

if (require(RColorBrewer)) {

pal <- c('#ff4d4d','#ccebff','#ffff1a','#1aff1a')
shape2 <- tm_shape(spdf)+
    tm_borders()+
    tm_polygons(col = 'political_trust_rating',title = legend_title, palette= pal)+
    tm_fill( title = expression("political trust rating")) +
    tm_scale_bar(breaks = c(0, 100, 200), text.size = 1)+
    tm_view( set.zoom.limits = c(3,8), set.view = c(lon = 10, lat = 56, zoom = 3), 
             alpha = .7,    view.legend.position = c("left","bottom")) 

shape2
}
# barchart with added parameters
barplot(spdf$political_trust_rating,
main = "Political Trust in European Countries",
ylab = "political trust rating",cex.axis = .7,
names.arg = spdf$NAME, font.axis = 1, col.axis = "Red", las = 0,cex.names = .7,
col = "blue",
las = 2)

In Europe, Switchzerland has a very high political satisfaction. Other countries such as Denmark, Malta, Finland, Norway, the Netherlands and Austria are also extremely pleased with politics in Europe.

Whereas countries such as Greece, Spain, Portugal, Slovakia and Italy have among other European countries the lowest level of political satisfaction.

3. Health in European Countries

#Good Health in European Countries

legend_title = expression("health percentage")

if (require(RColorBrewer)) {

pal <- c('#ff4d4d','#ccebff','#ffff1a','#1aff1a')
shape <- tm_shape(spdf)+
         tm_borders()+
        tm_polygons(col = 'prct_health_verygood',title = legend_title, palette= pal)+
         tm_fill( title = legend_title) +
         tm_scale_bar(breaks = c(0, 100, 200), text.size = 1)
        # tm_layout(title = "Good Health in European Countries")
shape + tm_view( set.zoom.limits = c(3,8), set.view = c(lon = 10, lat = 56, zoom = 3)) 
}
# barchart with added parameters
barplot(spdf$prct_health_verygood,
main = "Good Health in European Countries",
ylab = "percentage of health",cex.axis = .7,
names.arg = spdf$NAME, font.axis = 1, col.axis = "Red", las = 0,cex.names = .7,
col = "green",
las = 2)

Cyrus, Greece, Ireland, Iceland, Norway, Sweden, Switchzerland and the United Kingdom have very strong health outcomes.

Although countries like Estonia, Italy, Lithuania, Latvia, Portugal, and Turkey also have very low health rates across people, they have very high bad health levels. Pointing why these countries are not taking appropriate steps under People’s Well being and Supplements.

4. Pollution in European Countries

#Avg. Pollution in European Countries

legend_title = expression("Pollution")

if (require(RColorBrewer)) {

pal <- c('#ff4d4d','#ccebff','#ffff1a','#1aff1a')
shape <- tm_shape(spdf)+
         tm_borders()+
        tm_polygons(col = 'prct_rpt_pollution', palette= pal ,title = legend_title)+
         tm_fill( title = legend_title) +
         tm_scale_bar(breaks = c(0, 100, 200), text.size = 1)
        # tm_layout(title = "Avg. Pollution in European Countries")
shape + tm_view( set.zoom.limits = c(3,8), set.view = c(lon = 10, lat = 56, zoom = 3)) 
}
# barchart with added parameters
barplot(spdf$prct_rpt_pollution,
main = "Avg. Pollution in European Countries",
ylab = "pollution",cex.axis = .7,
names.arg = spdf$NAME, font.axis = 1, col.axis = "Red", las = 0,cex.names = .7,
col = "steelblue",
las = 2)

Malta, Germany and Turkey are obviously amongst other European countries the most populated. Whilst most European countries have low emission levels.

Whereas countries such as Finland, Switchzerland, Norway, Ireland and Denmark have very low emission levels.

5.Temperature in European Countries

#Avg. Temperature in European Countries

legend_title = expression("avg temperature")

if (require(RColorBrewer)) {
pal <- c('#ff4d4d','#ccebff','#ffff1a','#1aff1a')
shape <- tm_shape(spdf)+
         tm_borders()+
        tm_polygons(col = 'avg_temp', palette= pal,title = legend_title)+
         tm_fill( title = legend_title) +
         tm_scale_bar(breaks = c(0, 100, 200), text.size = 1)
         #tm_layout(title = "Avg. Temperature in European Countries")
shape + tm_view( set.zoom.limits = c(3,8), set.view = c(lon = 10, lat = 56, zoom = 3)) 
}
# barchart with added parameters
barplot(spdf$avg_temp,
main = "Avg. Temperature in European Countries",
ylab = "avg temperature",cex.axis = .7,
names.arg = spdf$NAME, font.axis = 1, col.axis = "Red", las = 0,cex.names = .7,
col = "orange",
las = 2)

Among all the European Countries Malta and Cyprus has recorded the highest tempereture because of nearer to Equator.
It is obvious that countries nearer to north pole will have lowest average temerature. Estonia, Finland and Icelands have the lowest temperature among all the European contries.

6. Savings in European Countries

#Low Savings in European Countries

legend_title = expression("savings")


if (require(RColorBrewer)) {

pal <- c('#ff4d4d','#ccebff','#ffff1a','#1aff1a')
shape <- tm_shape(spdf)+
         tm_borders()+
        tm_polygons(col = 'prct_low_savings', palette= pal ,title = legend_title)+
         tm_fill( title = legend_title) +
         tm_scale_bar(breaks = c(0, 100, 200), text.size = 1)
         #tm_layout(title = "Low Savings in European Countries")
shape + tm_view( set.zoom.limits = c(3,8), set.view = c(lon = 10, lat = 56, zoom = 3)) 
}
# barchart with added parameters
barplot(spdf$prct_low_savings,
main = "Low Savings in European Countries",
ylab = "savings",cex.axis = .7,
names.arg = spdf$NAME, font.axis = 1, col.axis = "Red", las = 0,cex.names = .7,
col = "yellow",
las = 2)

Countries like Belgium, Cyprus, Italy, Croatia, Latvia, Romania, Lithuania, Hungary and Italy are low savings European countries .

Countries such as Switchzerland, Luxembourg, Norway, Sweden, Malta are among the highest savings countries in Europe.

7. Job Satisfaction in European Countries

#High Job Satisfaction in European Countries

legend_title = expression("Job Satisfaction")


if (require(RColorBrewer)) {

pal <- c('#ff4d4d','#ccebff','#ffff1a','#1aff1a')
shape <- tm_shape(spdf)+
         tm_borders()+
        tm_polygons(col = 'prct_job_satis_high', palette= pal,title = legend_title)+
         tm_fill( title = legend_title) +
         tm_scale_bar(breaks = c(0, 100, 200), text.size = 1)
         #tm_layout(title = "High Job Satisfaction in European Countries")
shape + tm_view( set.zoom.limits = c(3,8), set.view = c(lon = 10, lat = 56, zoom = 3)) 
}
# barchart with added parameters
barplot(spdf$prct_job_satis_high,
main = "High Job Satisfaction in European Countries",
ylab = "Job Satisfaction",cex.axis = .7,
names.arg = spdf$NAME, font.axis = 1, col.axis = "Red", las = 0,cex.names = .7,
col = "blue",
las = 2)

Just two of the European countries, named Turkey and Bulgaria, have low work satisfaction. Except for these two, all other countries, particularly the Netherlands and Finland, are very satisfied and enjoying their job.

8. GDP Of European Countries

#GDP in European Countries

legend_title = expression("GDP (per Capita)")

if (require(RColorBrewer)) {
#pal <- brewer.pal(10, "Set2")[c(1, 8, 4, 5)]
pal <- c('#ff4d4d','#ccebff','#ffff1a','#1aff1a')
shape <- tm_shape(spdf)+
         tm_borders()+
         tm_polygons(col = 'gdp', palette= pal,title = legend_title)+
         tm_fill( title = legend_title) +
         tm_scale_bar(breaks = c(0, 100, 200), text.size = 1)
        # tm_layout(title = "GDP Of European Countries")
shape + tm_view( set.zoom.limits = c(3,8), set.view = c(lon = 10, lat = 56, zoom = 3)) 
  }
# barchart with added parameters
barplot(spdf$gdp,
main = "GDP in European Countries",
ylab = "gdp per capita",cex.axis = .7,
names.arg = spdf$NAME, font.axis = 1, col.axis = "Red", las = 0,cex.names = .7,
col = "skyblue",
las = 2)

Germany has the highest GDP in Europe, then Great Britain, France and Italy also have very high GDP relative to other European countries. Countries such as Spain and the Netherlands have strong GDP too.

In contrast to other European countries, some countries like Cyrus, Luxembourg, Hungary, Malta, Slovenia, Malta, Iceland have very small GDPs.

9. Crime in European Countries

#%age of Reported Crime in European Countries

legend_title = expression("Crime Reported")


if (require(RColorBrewer)) {
#pal <- brewer.pal(10, "Set2")[c(1, 8, 4, 5)]
pal <- c('#ff4d4d','#ccebff','#ffff1a','#1aff1a')
shape <- tm_shape(spdf)+
         tm_borders()+
        tm_polygons(col = 'prct_rpt_crime',  palette= pal,title = legend_title)+
         tm_fill( title = legend_title) +
         tm_scale_bar(breaks = c(0, 100, 200), text.size = 1)
        # tm_layout(title = "%age of Reported Crime in European Countries")
shape + tm_view( set.zoom.limits = c(3,8), set.view = c(lon = 10, lat = 56, zoom = 3)) 
}
# barchart with added parameters
barplot(spdf$prct_rpt_crime,
main = "%age of Reported Crime in European Countries",
ylab = "Crime Reported", cex.axis = .7,
names.arg = spdf$NAME, font.axis = 1, col.axis = "Red", las = 0,cex.names = .7,
col = "brown",
las = 2)

Crime reported in European countries is had a lot of variation. Bulgaria is out of the box and reported a very high rate of crimes in the country. Other countries have an almost similar rate of crime but there are few countries that are able to stop these crimes. Iceland reported the lowest crime rate followed by Croatia.

10. Population in European Countries

#population in European Countries

legend_title = expression("total population")

if (require(RColorBrewer)) {
#pal <- brewer.pal(10, "Set2")[c(1, 8, 4, 5)]
pal <- c('#ff4d4d','#ccebff','#ffff1a','#1aff1a')

shape <- tm_shape(spdf)+
         tm_borders()+
        tm_polygons(col = 'total_pop', palette= pal,title = legend_title)+
         tm_fill( title = 'Total Population') +
         tm_scale_bar(breaks = c(0, 100, 200), text.size = 1)
        # tm_layout(main.title = "population in European Countries" )#+tm_text(text = 'total_pop')
shape+ tm_view( set.zoom.limits = c(3,8), set.view = c(lon = 10, lat = 56, zoom = 3)) 

}
# barchart with added parameters
 barplot(spdf$total_pop,
main = "Population in European Countries",
ylab = "total population", cex.axis = .7,
names.arg = spdf$NAME, font.axis = 1, col.axis = "Red", las = 0,cex.names = .7,
col = "steelblue",
las = 2)

Germany is the Highest populates country among all the european contries. France, United Kingdome and Itly are also in the same list. There are also some contries which have very low density of population. Iceland followed by Luxembourg is at the top in this list.

CONCLUSION :

It is always good to compare or analyze data graphically which is easy to understand and also looks good. A normal person can easily understand by visualizing the features and comparing them. There are other parameters also present which can be compared among the European countries but due to lack of data and time we just tried a few features.

REFERENCES:

[1] Sharma, R., 2020. Comparing European Countries !!!. [online] Kaggle.com. Available at: https://www.kaggle.com/roshansharma/comparing-european-countries [Accessed 4 April 2020].

[2] En.wikipedia.org. 2020. Europe. [online] Available at: https://en.wikipedia.org/wiki/Europe [Accessed 4 April 2020].